Unfortunately, there are not enough studies relevant to live streaming shopping in the context of Malaysia. This article aimed to study the influencing factors of impulse buying decisions on live stream shopping.
RESEARCH OVERVIEW
- Introduction
- Research Background
- Problem Statement
- Research Questions
- General Question
- Specific Questions
- Research Objectives
- Research Scope
- Research Significance
- Contribution of Study
- Summary
What are the inferential factors affecting consumers' impulse purchase decision during live streaming shopping in Malaysia? Exploring the inferential factors that influence consumers' impulse purchase decision during live streaming shopping.
LITERATURE REVIEW
Introduction
Underlying Theory
- Consumer Behaviour
There are different types of buying behavior such as extended decision making, limited decision making, impulse buying and routine response. First, routine response is defined as consumers will buy a brand when they have previously tried and are familiar with it.
Research Model
- Stimulus-Organism-Response Model (S-O-R)
This is partly because many previous studies of online consumer behavior have been successfully applied using the S-O-R model. In an online retail environment, previous research has continuously studied the relationship between a consumer's cognitive and emotional process, as well as the resulting behavior (e.g., Eroglu et al., 2001; Lee et al., 2021; Chen & Yao, 2018; Ming et al., 2021).
Review Variables
- Stimulus in Live Streaming Shopping
- Price Promotion
- Time Pressure
- Interpersonal Interaction
- Visual Appeal
- Organism in Live Streaming Shopping
- Perceived Risk
- Response in Live Streaming Shopping
- Impulse Buying Decision
Most importantly, the promotion in the live streaming room is limited and stopped immediately after the time expires. Thus, the interactive live store setup consists of live streamers, consumers and real-time information in the live broadcast room.
Research Domain
- Evolution of Live Streaming Shopping
- Type of live streaming commerce
March: Mogujie launched the feature of live streaming trading. May: Taobao began to launch live broadcast function; Kuaishou launched live broadcast feature. November: Douyin launched live broadcasting function; during the same period, the Taobao live broadcast accumulated more than 100 million views in a single day. February: Taobao launches Taobao Live APP April: WeChat piloted live broadcast e-commerce. August: Kaola launched live broadcast feature.
Taobao Live, Mogujie and other platforms have cooperated with MCN institutions to promote the development of live e-commerce business throughout the growth period.
Conceptual Framework
Traditional e-commerce platforms are represented by Taobao; entertainment content platforms represented by Douyin; and shopping guide community platforms is presented by Mogujie in a live e-commerce environment as shown in Table 2.3 above.
Hypotheses Development
- The relationship between Price Promotion (PP) and Impulse Buying
- The relationship between Time Pressure and Impulse Buying Decision
- The relationship between Interpersonal Interaction and Impulse Buying
- The relationship between Perceived Risk (PR) and Impulse Buying
- Perceived Risk (PR) mediates the relationship between Price Promotion
- Perceived Risk (PR) mediates the relationship between Time Pressure
- Perceived Risk (PR) mediates the relationship between Interpersonal
- Perceived Risk (PR) mediates the relationship between Visual Appeal
The degree of willingness or desire to purchase a product influences the association between perceived risk and impulse buying. Eun Lee & Stoel (2014) stated that perceived risk would mediate the relationship between price promotion and impulsive behavior. Huang & Suo (2021) also found that price promotion will negatively affect consumer's perceived risk and will mediate the relationship between price promotion and impulsive behavior.
Because the consumer's perceived risk in this condition is lower than in the case of a smaller time limit, the promotion time limit can help reduce the consumer's perceived risk and increase impulse buying (Zhang et al., 2022).
Summary
METHODOLOGY
- Introduction
- Research Design
- Sampling Design
- Target Population
- Sampling Frame and Sampling Location
- Sampling Technique
- Sampling Size
- Data Collection Methods
- Primary Data
- Research Instrument
- Questionnaire Design
- Pilot Test
- Evaluation of Measurement and Structural Model
- Scale of Measurement
- Data Processing
- Data Checking
- Data Coding
- Data Analysis Technique
- Descriptive Analysis
- Inferential Analysis
- Mediation Analysis
- Summary
Sample frames are not used as this study is open to any individual 18 years of age or older who has had an in-person shopping experience. The target population in this research is individuals aged 18 years who have experienced live shopping in Malaysia. The questionnaire will also include a screening question such as the experience of purchasing a product in live streaming shopping.
After the pilot test, the questionnaire will be distributed to the people who have experience with live streaming shopping through Google Form.
DATA ANALYSIS
Introduction
Demographic of Respondent
- Gender
- Age
- Education Level
- Occupation
He found that the majority of respondents have a bachelor's degree, counting respondents), with degrees for respondents), master's degrees for respondents), high school or less. This shows that most of the respondents are busy interviewed students), followed by office workers with respondents), self-.
Screening Question
- Bought Product from Live Streaming Shopping
General Question
- Platform for Live Streaming Shopping
- Frequency of Live Streaming Shopping
- Time of Watching Live Streaming Shopping
Based on Figure 4.6, it showed the platform used for live shopping by the respondents. Based on Figure 4.7, it shows the percentage of live streaming shopping frequency of the respondents. The majority of our respondents' frequency of live streaming purchases is at least once per month.
Based on Figure 4.8 above, the majority of respondents watch live streaming shopping for 30 minutes to 1 hour, which occupied the respondents).
Descriptive Statistic of Variable
- Mean and Standard Deviation for Price Promotion (PP)
- Mean and Standard Deviation for Promotion Time Limit (PTL)
- Mean and Standard Deviation for Perceived Opportunity Cost (POC) 54
- Mean and Standard Deviation for Consumer-Consumer Interaction (CCI)
- Mean and Standard Deviation for Visual Appeal (VA)
- Mean and Standard Deviation for Perceived Risk (PR)
- Mean and Standard Deviation for Impulse Buying Decision (IBD)
The mean and standard deviation values for the PP variable in the questionnaire are shown in Table 4.9. The mean and standard deviation values for the CSI variable in the questionnaire are shown in Table 4.12. The mean and standard deviation values for the VA variable in the questionnaire are shown in Table 4.14.
The mean and standard deviation values for the PR variable in the questionnaire are presented in table 4.15.
Multivariate Assumption Test
The purpose of this reliability test is to determine the consistency and stability of the study variables (Achour, 2017). Total of 254 sets of questionnaires were collected, but 58 sets are discarded due to inexperienced live streaming shopping. The result implied that promotional period, consumer-consumer interaction and perceived risk falling between 0.70 and 0.80 are respectable.
For price promotion, consumer-streamer interaction, visual appeal, perceived opportunity cost, and impulse purchase fall between 0.80 to 0.90, indicating a very good level of reliability (Achour, 2017).
Pearson Correlations
Therefore, 196 sets of questionnaires were included in this reliability test and the results will be presented in the table below. Because the p-value is less than 0.05, all relationships between independent factors, mediators, and dependent variables H1 through H13 are significant, as shown in Table 4.18. H1, H2, and H3 have correlation coefficients of 0.50 to 0.70, indicating a moderately positive correlation, but H4, H5, and H6 have correlation coefficients of 0.30 to 0.50, indicating a moderately positive correlation.
Furthermore, the correlation between perceived risk and impulse purchase choice (H7) is moderately positive, varying from 0.50 to 0.70.
Multiple Regression
- Influencing Factors on Impulse Buying Decision
- Influencing Factors on Perceived Risk
- Perceived Risk Towards Impulse Buying Decision
Table 4.21 above shows that 3 of the 6 independent variables, namely price promotion, perceived opportunity costs, and time limit for promotions, have a significant relationship with consumers' impulse purchase decision to shop via live streaming. Table 4.24 above shows that two of the six independent variables, namely the promotion time limit and the perceived opportunity cost, have a significant relationship to the independent variables related to the perceived risk. As an illustration, each unit increase in price promotion will increase impulse purchase choice on live streaming by 0.0330 units, as will other independent variables.
According to Table 4.25, R2 is 0.389, which suggests that about 38.9% of the dependent variable (impulse buying choice) is significant and can be attributed to the mediator (perceived risk).
Mediation Analysis
- Perceived Risk Mediation Analysis
If the regression coefficient of the independent variable on the dependent variable is not significant after adding the mediating variable, there will be no mediating variable. If the regression coefficient of the independent variable to the dependent variable is significant, but the value of the regression coefficient decreases, then the mediators play a part of the mediating role. It also has the chance of zero occurring between the upper and lower bounds of the 95% confidence interval.
However, perceived risk has a mediating effect between price promotion, promotion time frame, and impulse buying decision.
Hypotheses Testing
DISCUSSION, CONCLUSION AND IMPLICATIONS
Introduction
Summary of Statistical Analysis
- Descriptive Analysis
- Demographic Profile
- Scale Measurement
Furthermore, the relationship between perceived risk and impulsive buying decision (H7) is between 0.50 and 0.70, indicating a moderate positive correlation.
Discussion of Major Findings
- Relationship between Stimulus factors and Response factors
- Relationship between Organism factors (Perceived Risk) toward
- Organism factors (perceived risk) has mediating effect between Stimulus
Based on the above result, price promotion is the most important effect on the impulse buying decision. In addition, the promotional time limit and the perceived opportunity cost significantly influenced the impulse buying decision. The finding indicates that perceived risk has a mediating effect between PP, PTL and POC toward an impulse purchase decision.
Nevertheless, the result showed that perceived risk has no mediating effect between CSI, CCI, VA towards impulse buying decision.
Implication of the Study
- Theoretical Implication
- Practical Implication
For consumer-streamer interaction and consumer-consumer interaction, one of the possible reasons may be that buyers will acquire product information and build up positive sentiments, such as trust in some of the brand or seller during the live streaming shopping. Not only that, but the result also shows that perceived risk has no mediating effect between visual appeal and impulsive buying decision. Lee & Chen (2021) believed that impulse purchases are made without regard to financial or other considerations in live streaming commerce.
There are several practical implications and profitable strategies for sellers, traders and merchants for live streaming trading.
Limitation of the Study
Managers must use a variety of techniques to lower the level of perceived risk of buyers. Factors such as POC, PTL, PP, VA, CSI and CCI can lower the buyer's perceived risk. Retailers can also encourage transactions agreed between the seller and the buyer by providing related services such as product warranty, 7 days no reason to return, cargo risks, etc. to reduce the perceived risk of the buyer and increase their desire to buy (Huang & Suo, 2021).
In addition, this research focuses more on external factors such as PP, PTL, POC, CSI, CCI, and VA only on consumers' impulsive buying decision instead of product perspective, and using perceived risk as a mediating variable to impulsive buying behavior.
Recommendation of Future Research
There is a limit of respondents from people of different countries, age groups, occupations and education levels. It was thought that distributions made by posting a link on social media only garnered responses from people in related social groups. Finally, researchers can broaden the scope of the study and expand the sample size in the future.
To avoid responses from people of the same gender, ethnicity, occupation and educational level, questionnaires should not be distributed privately.
Conclusion
A_Literature_Review/links/57e9882308aef8bfcc961cce/Encouraging-Factors-of-Impulse-Buying-Behavior-A-Literature-Review.pdf. The study of the effect of virtual brand community interaction on impulse buying: the moderating role of self-construction. Title: An Investigation of Consumers' Impulse Buying Decision: Live Streaming Shopping on a Cross-Border Ecommerce Platform.
The aim of this questionnaire is to investigate consumers' impulse purchase decision: live streaming shopping on a cross-border e-commerce platform.
